Faculty of Information Technology, BUT

Course details

Knowledge Discovery in Databases

ZZD Acad. year 2001/2002 Winter semester 5 credits


Language of instruction



Examination (oral)

Time span

39 hrs lectures, 13 hrs projects

Assessment points

50 exam, 50 projects



Syllabus of lectures

  • Introduction - motivation, fundamental concepts, data source and knowledge types.
  • Data Warehouse and OLAP Technology for Data Mining.
  • Data Preparation.
  • Data Mining Systems - task specification, data mining query languages, system architectures.
  • Concept Description: Characterization and Comparison.
  • Mining Association Rules in Transaction Data.
  • Mining Association Rules in Relational Databases and Warehouses.
  • Classification - decision tree, Bayesian classification, using neural networks for classification.
  • Other Classification Methods. Prediction.
  • Cluster Analysis.
  • Mining Complex Types of Data - data mining inobject, spatial, and text data.
  • Mining in Multimedia Data, Time Sequences, and Mining the WWW.
  • Applications and Trends in Data Mining.

Syllabus - others, projects and individual work of students

Reading up and treatment of a selected scientific paper concerning knowledge discovery in a field related to the student's PhD thesis.
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